Snowflake connector

The Snowflake connector allows querying and creating tables in an external Snowflake account. This can be used to join data between different systems like Snowflake and Hive, or between two different Snowflake accounts.

Configuration

To configure the Snowflake connector, create a catalog properties file in etc/catalog named, for example, example.properties, to mount the Snowflake connector as the snowflake catalog. Create the file with the following contents, replacing the connection properties as appropriate for your setup:

connector.name=snowflake
connection-url=jdbc:snowflake://<account>.snowflakecomputing.com
connection-user=root
connection-password=secret
snowflake.account=account
snowflake.database=database
snowflake.role=role
snowflake.warehouse=warehouse

Arrow serialization support

This is an experimental feature which introduces support for using Apache Arrow as the serialization format when reading from Snowflake. Please note there are a few caveats:

• Using Apache Arrow serialization is disabled by default. In order to enable it, add --add-opens=java.base/java.nio=ALL-UNNAMED to the Trino JVM config.

Multiple Snowflake databases or accounts

The Snowflake connector can only access a single database within a Snowflake account. Thus, if you have multiple Snowflake databases, or want to connect to multiple Snowflake accounts, you must configure multiple instances of the Snowflake connector.

Type mapping

Because Trino and Snowflake each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.

List of Snowflake data types.

Snowflake type to Trino type mapping

The connector maps Snowflake types to the corresponding Trino types following this table:

Snowflake type to Trino type mapping

Snowflake type	Trino type	Notes
BOOLEAN	BOOLEAN
INT, INTEGER, BIGINT, SMALLINT, TINYINT, BYTEINT	DECIMAL(38,0)	Synonymous with NUMBER(38,0). See Snowflake data types for fixed point numbers for more information.
FLOAT, FLOAT4, FLOAT8	DOUBLE	The names FLOAT, FLOAT4, and FLOAT8 are for compatibility with other systems; Snowflake treats all three as 64-bit floating-point numbers. See Snowflake data types for floating point numbers for more information.
DOUBLE, DOUBLE PRECISION, REAL	DOUBLE	Synonymous with FLOAT. See Snowflake data types for floating point numbers for more information.
NUMBER	DECIMAL	Default precision and scale are (38,0).
DECIMAL, NUMERIC	DECIMAL	Synonymous with NUMBER. See Snowflake data types for fixed point numbers for more information.
VARCHAR	VARCHAR
CHAR, CHARACTER	VARCHAR	Synonymous with VARCHAR except default length is VARCHAR(1). See Snowflake String & Binary Data Types for more information.
STRING, TEXT	VARCHAR	Synonymous with VARCHAR. See Snowflake String & Binary Data Types for more information.
BINARY	VARBINARY
VARBINARY	VARBINARY	Synonymous with BINARY. See Snowflake String & Binary Data Types for more information.
DATE	DATE
TIME	TIME
TIMESTAMP_NTZ	TIMESTAMP	TIMESTAMP with no time zone; time zone, if provided, is not stored. See Snowflake Date & Time Data Types for more information.
DATETIME	TIMESTAMP	Alias for TIMESTAMP_NTZ. See Snowflake Date & Time Data Types for more information.
TIMESTAMP	TIMESTAMP	Alias for one of the TIMESTAMP variations (TIMESTAMP_NTZ by default). This connector always sets TIMESTAMP_NTZ as the variant.
TIMESTAMP_TZ	TIMESTAMP WITH TIME ZONE	TIMESTAMP with time zone.

No other types are supported.

Trino type to Snowflake type mapping

The connector maps Trino types to the corresponding Snowflake types following this table:

Trino type to Snowflake type mapping

Trino type	Snowflake type	Notes
BOOLEAN	BOOLEAN
TINYINT	NUMBER(3, 0)
SMALLINT	NUMBER(5, 0)
INTEGER	NUMBER(10, 0)
BIGINT	NUMBER(19, 0)
REAL	DOUBLE
DOUBLE	DOUBLE
DECIMAL	NUMBER
VARCHAR	VARCHAR
CHAR	VARCHAR
VARBINARY	BINARY
VARBINARY	VARBINARY
DATE	DATE
TIME	TIME
TIMESTAMP	TIMESTAMP_NTZ
TIMESTAMP WITH TIME ZONE	TIMESTAMP_TZ

No other types are supported.

Type mapping configuration properties

The following properties can be used to configure how data types from the connected data source are mapped to Trino data types and how the metadata is cached in Trino.

Property name	Description	Default value
unsupported-type-handling	Configure how unsupported column data types are handled: IGNORE, column is not accessible. CONVERT_TO_VARCHAR, column is converted to unbounded VARCHAR. The respective catalog session property is unsupported_type_handling.	IGNORE
jdbc-types-mapped-to-varchar	Allow forced mapping of comma separated lists of data types to convert to unbounded VARCHAR

SQL support

The connector provides read access and write access to data and metadata in a Snowflake database. In addition to the globally available and read operation statements, the connector supports the following features:

• INSERT

• DELETE

• TRUNCATE

• CREATE TABLE

• CREATE TABLE AS

• DROP TABLE

• ALTER TABLE

• CREATE SCHEMA

• DROP SCHEMA

Procedures

system.flush_metadata_cache()

Flush JDBC metadata caches. For example, the following system call flushes the metadata caches for all schemas in the example catalog

USE example.example_schema;
CALL system.flush_metadata_cache();

system.execute('query')

The execute procedure allows you to execute a query in the underlying data source directly. The query must use supported syntax of the connected data source. Use the procedure to access features which are not available in Trino or to execute queries that return no result set and therefore can not be used with the query or raw_query pass-through table function. Typical use cases are statements that create or alter objects, and require native feature such as constraints, default values, automatic identifier creation, or indexes. Queries can also invoke statements that insert, update, or delete data, and do not return any data as a result.

The query text is not parsed by Trino, only passed through, and therefore only subject to any security or access control of the underlying data source.

The following example sets the current database to the example_schema of the example catalog. Then it calls the procedure in that schema to drop the default value from your_column on your_table table using the standard SQL syntax in the parameter value assigned for query:

USE example.example_schema;
CALL system.execute(query => 'ALTER TABLE your_table ALTER COLUMN your_column DROP DEFAULT');

Verify that the specific database supports this syntax, and adapt as necessary based on the documentation for the specific connected database and database version.

Table functions

The connector provides specific table functions to access Snowflake.

query(varchar) -> table

The query function allows you to query the underlying database directly. It requires syntax native to Snowflake, because the full query is pushed down and processed in Snowflake. This can be useful for accessing native features which are not available in Trino or for improving query performance in situations where running a query natively may be faster.

Find details about the SQL support of Snowflake that you can use in the query in the Snowflake SQL Command Reference, including PIVOT, lateral joins and other statements and functions.

The native query passed to the underlying data source is required to return a table as a result set. Only the data source performs validation or security checks for these queries using its own configuration. Trino does not perform these tasks. Only use passthrough queries to read data.

As a simple example, query the example catalog and select an entire table:

SELECT
  *
FROM
  TABLE(
    example.system.query(
      query => 'SELECT
        *
      FROM
        tpch.nation'
    )
  );

As a practical example, you can use the Snowflake SQL support for PIVOT to pivot on all distinct column values automatically with a dynamic pivot.

SELECT
  *
FROM
  TABLE(
    example.system.query(
      query => '
        SELECT *
        FROM quarterly_sales
          PIVOT(SUM(amount) FOR quarter IN (ANY ORDER BY quarter))
        ORDER BY empid;
      '
    )
  );

Note

The query engine does not preserve the order of the results of this function. If the passed query contains an ORDER BY clause, the function result may not be ordered as expected.

Performance

The connector includes a number of performance improvements, detailed in the following sections.

Pushdown

The connector supports pushdown for a number of operations:

• Limit pushdown

• Top-N pushdown

Aggregate pushdown for the following functions:

• avg()

• count()

• max()

• min()

• sum()

• stddev()

• stddev_pop()

• stddev_samp()

• variance()

• var_pop()

• var_samp()

• covar_pop()

• covar_samp()

• corr()

• regr_intercept()

• regr_slope()

Note

The connector performs pushdown where performance may be improved, but in order to preserve correctness an operation may not be pushed down. When pushdown of an operation may result in better performance but risks correctness, the connector prioritizes correctness.